提出一种几何无关的反事实解释方法，可生成更接近真实且符合约束的个性化治疗建议。
Towards Personalized Treatment Plan: Geometrical Model-Agnostic Approach to Counterfactual Explanations
- 通过离散化边界采样与二分搜索，高效逼近决策边界以找到最近反事实点。
- 在四个数据集上，反事实距离减少5%至50%（L2范数），性能优于现有方法。
- 支持年龄、性别等不可变特征约束，适合医疗等现实场景的个性化决策解释。
本文提出一种高维空间中生成反事实解释的方法，包含四个步骤：拟合数据、定位决策边界、设定问题约束、计算边界最近点（即反事实解释）。我们采用离散化策略，在边界上采样多个离散点，再通过二分搜索定位决策边界点，并寻找最近可行解。该方法称为《分段采样边界近似》（SSBA），在四个不同维度的数据集上均表现优异，反事实解释的L2距离相较现有方法降低5%至50%。同时，方法能有效处理真实约束，如限制年龄、性别、身高等不可变或类别型特征的变化。相比网格法，运行时间快多个数量级。整体而言，该方法为模型无关的反事实生成提供了简单有效的解决方案，可生成满足约束的最接近可行解。所有代码与结果见：https://github.com/dsin85691/SSBA_For_Counterfactuals。
原文摘要 · Abstract (English)
In our article, we describe a method for generating counterfactual explanations in high-dimensional spaces using four steps that involve fitting our dataset to a model, finding the decision boundary, determining constraints on the problem, and computing the closest point (counterfactual explanation) from that boundary. We propose a discretized approach where we find many discrete points on the boundary and then identify the closest feasible counterfactual explanation. This method, which we later call $\textit{Segmented Sampling for Boundary Approximation}$ (SSBA), applies binary search to find decision boundary points and then searches for the closest boundary point. Across four datasets of varying dimensionality, we show that our method can outperform current methods for counterfactual generation with reductions in distance between $5\%$ to $50\%$ in terms of the $L_2$ norm. Our method can also handle real-world constraints by restricting changes to immutable and categorical features, such as age, gender, sex, height, and other related characteristics such as the case for a health-based dataset. In terms of runtime, the SSBA algorithm generates decision boundary points on multiple orders of magnitude in the same given time when we compare to a grid-based approach. In general, our method provides a simple and effective model-agnostic method that can compute nearest feasible (i.e. realistic with constraints) counterfactual explanations. All of our results and code are available at: https://github.com/dsin85691/SSBA_For_Counterfactuals
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